Examining external control arms in oncology: A scoping review of applications to date

Abstract Objectives Randomized controlled trials (RCTs) are the gold standard for evaluating the comparative efficacy and safety of new cancer therapies. However, enrolling patients in control arms of clinical trials can be challenging for rare cancers, particularly in the context of precision oncology and targeted therapies. External Control Arms (ECAs) are a potential solution to address these challenges in clinical research design. We conducted a scoping review to explore the use of ECAs in oncology. Methods We systematically searched four databases, namely MEDLINE, EMBASE, Web of Science, and Scopus. We screened titles, abstracts, and full texts for eligible articles focusing on patients undergoing therapy for cancer, employing ECAs, and reporting clinical outcomes. Results Of the 629 articles screened, 23 were included in this review. The earliest included studies were published in 1996, while most studies were published in the past 5 years. 44% (10/23) of ECAs were employed in blood‐related cancer studies. Geographically, 30% (7/23) of studies were conducted in the United States, 22% (5/23) in Japan, and 9% (2/23) in South Korea. The primary data sources used to construct the ECAs involved pooled data from previous trials (35%, 8/23), administrative health databases (17%, 4/23) and electronic medical records (17%, 4/23). While 52% (12/23) of the studies employed methods to align treatment and ECAs characteristics, 48% (11/23) lacked explicit strategies. Conclusion ECAs offer a valuable approach in oncology research, particularly when alternative designs are not feasible. However, careful methodological planning and detailed reporting are essential for meaningful and reliable results.


| INTRODUCTION
1][12] As a result, employing active controls (i.e., standard of care) rather than placebos has been recommended when conducting clinical trials. 4,13However, the rapid evolution of new cancer therapies often leads to changes in standards of care, potentially disrupting the trial equipoise and affecting the capacity for meaningful comparisons. 4,146][17] An ECA is an umbrella term encompassing various types of controls that are used when randomization is unfeasible or unethical. 18External controls are classified as concurrent controls, enrolled and treated simultaneously with the experimental arm but in a distinct setting, and non-concurrent controls, collected from retrospective data or studies. 19,20Also known as synthetic control arms (SCAs) or historical controls, these arms can act as comparator groups by utilizing external data that have been selected to match the population characteristics in the treatment arm. 18,21Data for these controls can be extracted from electronic medical records, publicly available databases, registries, peer-reviewed literature or other sources. 43][24][25] While ECAs offer distinct advantages, their inherent limitations must also be considered.For example, the reliance on historical or external data sources can introduce various biases, including selection bias and confounding. 4,15,20In addition, as ECAs' baseline randomization is absent in single-arm trials, ensuring appropriate design of ECAs is crucial for comparability of patients' baseline risk levels. 15Therefore, a comprehensive evaluation of factors like data source, data quality, statistical methods used for matching, and the relevance and reliability of variables measured is necessary to construct methodologically sound ECAs. 4,6,26,27rthermore, the application of health data records or real-world registries as synthetic controls is becoming increasingly popular in clinical research and has been recognized by various international regulatory bodies, such as the United States Food and Drug Administration (FDA), the Canadian Agency for Drugs and Technologies in Health, as well as the National Institute for Health and Care Excellence. 4,28Yet no standard guidelines have been recognized for constructing and/or using synthetic or external control arms.These regulatory bodies, however, have expressed interest in real-world evidence, suggesting that future guidelines may include more detailed guidance on ECA use.For example, the FDA approved cerliponase alfa treatment for a specific form of Batten disease based on a synthetic control study which compared data from 22 patients in a single-arm trial with 42 untreated patients. 4ICE appraised 22 individual pharmaceutical technologies by employing ECAs to assess comparative clinical efficacy.While the majority (59%) of these ECAs incorporated published RCT data for their external control, 27% relied on observational data. 29he rationale for conducting this scoping review arises from the growing utilization of ECAs in oncology research, aiming to address the challenges encountered in traditional RCTs.Unlike individual literature reviews that often focus on specific aspects of ECAs, such as applications, limitations, data sources, methods, and designs, this scoping review aims to provide a comprehensive synthesis that integrates insights and outcomes from various studies utilizing ECAs in their research design.By consolidating and synthesizing the existing literature, we aim to describe current applications and inform future study designs.

| METHODS
We conducted a scoping review instead of a systematic review in compliance with the guidelines presented by Munn et al. 30 The database was first searched on November 10, 2022, while a manual search was conducted on December 10, 2022.Considering the increasing adoption of ECAs in research design, we aimed to explore the current knowledge and evidentiary base in this specific field.

| Search strategy and selection criteria
We systematically searched four electronic databases, including MEDLINE (Ovid), EMBASE, Web of Science, and Scopus, to identify relevant peer-reviewed studies on this topic.Our search strategy was validated by a librarian (C.M.) at the University of Calgary Health Sciences Library.
The strategy consisted of the index keywords (cancer and synthetic control arms), along with their associated MeSH and iterative search terms (Table S1).We did not apply language restrictions to ensure that relevant studies published in other languages were included in the synthesis.

| Eligibility assessment
We employed a broad and inclusive approach to our eligibility criteria, with the intention of encompassing a wide array of articles that highlight the applications of ECAs in oncology.To be included in our review, an article must satisfy the following criteria: (1) involved patients diagnosed with cancer of any type, (2) employed a synthetic control arm in the design, and (3) reported clinical outcome(s) in the treatment arm and the synthetic control arm.We excluded literature reviews, gray literature, posters, and commentaries.Studies focusing on the assessment of multiple trials concurrently, rather than evaluating the efficacy of a single intervention from a specific single arm/cohort study, were also excluded.These included master protocol basket trials (i.e., basket and umbrella trials) which we made references to in the discussion section to provide context.Lastly, we excluded articles centered on the methodology of executing ECAs, including those addressing the matching of baseline characteristics.
We imported all records into EndNote X9 31 reference management software to remove any duplicates.The remaining records were then transferred to the COVIDENCE web platform. 32We further filtered out any remaining duplicates and initiated title and abstract screening.In the first round, two reviewers (E.F. and M.K.) independently screened the titles and the abstracts of the included records.Discrepancies, accounting for only 5% (31/629) of all studies, were resolved through consensus.Following primary screening, full-text eligibility screening was performed and validated independently.Reference lists of records included in the full-text eligibility screening were manually searched and validated for additional records that met the eligibility criteria.

| Data abstraction
Data from all included records were independently abstracted by the same two reviewers.One reviewer then verified all abstracted data and discrepancies were resolved through consensus.From all included articles, we extracted the following variables: authors' names, cancer site and type, stage, source of real-world data used to construct the ECAs, and study design.We also gathered data on the countries where the studies were conducted.These locations also coincided with the areas from which the patient experimental group was enrolled (Table 1).We also extracted additional variables related to the study design and the statistical methods used.These included specific covariates used for adjustment, the methods employed for such adjustments, primary outcomes in their respective units (i.e., odds ratio, percentage, time, etc.), and their definitions.Moreover, we retrieved pooled estimates with their 95% confidence intervals and their corresponding p-values (Table 2).
We extracted more granular information in the supplementary materials including sample size, aim of each study and the specific inclusion and exclusion criteria that were applied.We also recorded the date of data collection, the type of intervention administered, and the line of therapy (first line, second line, etc.) (Table S2).

| Studies Identified
Figure 1 presents the search results for relevant literature and the screening process.Of the 629 records identified, 303 duplicates were removed.Based on title and abstract, 188 were excluded, leaving 139 full-text articles to be retrieved and assessed for eligibility.Of these, 18 were included and 121 were excluded for reasons outlined in Figure 1.An additional 5 papers were manually included, for a total of 23 articles in this scoping review.

| Data collection timeline
We observed that the majority of studies, with a few exceptions, maintained a close temporal alignment between the data collection periods for the ECAs and the treatment arm.]46,50,52 Furthermore, 11% (2/18) of studies examined second-line (≥2 L) therapy subsequent treatment stages. 34,495]48 Moreover, 9% (2/23) of the studies 33,51 did not explicitly report the lines of therapy (Table S2).Whereas 13% (3/23) of the studies focused on screening procedures. 28,38,47

| DISCUSSION
The adoption of ECAs has been driven by the challenges faced by traditional RCTs, particularly in studies involving rare cancers or precision medicine.While ECAs offer solutions to certain limitations in clinical research, they also present methodological challenges.Although these challenges are not comprehensively discussed in this paper, they generally stem from concerns related to data source reliability, potential biases, and the comparability of controls.The aim of this scoping review was to investigate the breadth of research conducted in the scope of ECAs within oncology and to provide a thorough summary of the peer-reviewed studies in this area.After rigorous screening, 23 papers were included, all of which specifically integrated ECAs as comparator groups for a single intervention arm.Our analysis underscores a notable increase in the adoption of ECAs in oncology research, with 65% (18/23) of our articles published since the 2010s.These findings

Eligibility
align with similar studies indicating a surge in publications over past two decades. 17,19,54This rise may reflect a broader acceptance of ECAs by both pharmaceutical companies and health technology assessment agencies. 4,18,19,55,5652,53 In addition, lung cancers, including the ALK-positive non-small-cell and RET Fusion-positive variations, constituted about 9% (2/23) of the studies. 26,44This upward shift appears to align with the growing emphasis on biomarker testing and targeted therapies. 4,57With tumors now frequently categorized based on genetic profiles, leading to more refined phenotypic categorization, conventional trials are facing recruitment challenges. 58,59Further, approximately 43% (10/23) of the studies focused on advanced cancer stages (III-IV).This could be attributed to the unique characteristics of advanced-stage patients, who may not always be optimal candidates for traditional RCTs due to limited patient pools, specific therapy needs, among other factors.Moreover, approximately 30% (7/23) of the studies were conducted in the United States which is consistent with the country's abundant resources, robust research infrastructure, and extensive initiatives in trial designs. 60ased on our findings, the majority of studies (30%, 7/23) utilized pooled data from past trials as primary data sources for constructing ECAs.This observation is consistent with other studies that emphasized the use of external control data from recent RCTs, since they uphold a high standard of data collection and are preferred for constructing reliable ECAs. 4,61Contrarily, 17% (4/23) of the studies relied on administrative health databases, while another 17% (4/23) utilized electronic medical records as their data sources.These data types, particularly non-clinical trial data, although they reflect real-world clinical practice, they might not guarantee the controlled environment typical of RCTs, potentially introducing heterogeneity and bias. 4,24,62ith respect to matching control data to the intervention group, 26% (6/23) of the studies applied propensity score weighting for adjustments, indicating a focus on achieving balanced and comparable patient cohorts.However, nearly 48% (11/23) of studies did not provide clear information on covariate adjustments-this concern has been underscored in the existing literature as it relates to the failure in accounting for baselines imbalances. 4,15,17,63,64As for the data collection timeline, most studies aimed for a close temporal alignment between the ECAs and the treatment arm.However, temporal gaps in some instances indicate potential challenges, possibly related to data accessibility.
Additional instances of ECAs used to provide contextualization for single-arm trials have been retrospectively demonstrated in the reanalyses of clinical trials across various cancer sites, including leukemia, Merkel cell carcinoma, myeloma, non-small cell lung cancer, and earlystage hormone receptor-positive breast cancer. 21,61,65,66imilarly, the incorporation of ECAs derived from realworld data to supplement single-arm oncology trials for regulatory approval and reimbursement decisions has gained popularity. 67 Various research groups have examined methodological challenges related to ECA design through regulatory case studies, proposing strategies to address these challenges. 65,68However, the absence of comprehensive guidance for optimal ECA development from regulatory bodies and health technology assessment (HTA) agencies has led to ambiguity in ECA design principles and uncertainty regarding their consistent acceptance by regulators and HTAs.Establishing reporting guidelines for the use of ECAs should be a priority, particularly as master protocol trials become more prevalent, and the number of single-arm studies continues to rise.Such guidelines could facilitate standardization and promote consistency in the implementation of ECAs.
While our scoping review, is comprehensive, it is not without limitations.which should be acknowledged.First, the scope of our search strategy might have inadvertently excluded relevant studies due to the specific terms and databases used.Although we aimed for inclusivity, it is possible that some pertinent research was missed, potentially affecting the comprehensiveness of our findings.Additionally, the exclusion of gray literature, posters, and commentaries might have omitted valuable insights or methodological details not covered by peer-reviewed articles.In our review, we highlighted the use of ECAs within oncology research, acknowledging the growing application of this study design.After our search cut-off date of November 10, 2022, additional pertinent studies have been published.These include a study on metastatic colorectal cancer and another on ovarian cancer, both of which evaluated the efficacy of immunotherapy in a nonrandomized setting using ECAs. 69,70These studies exemplify the continued evolution in the application of ECAs and highlight the potential in contexts where RCTs may not be feasible.The publication of these studies post our search date should be acknowledged as a limitation in our review.Their inclusion in future reviews could provide a more comprehensive understanding of the ECA approach in evaluating the efficacy of emerging therapies like immunotherapy, especially in advanced cancer stages where traditional RCTs face challenges in recruitment.Furthermore, we did not to assess the quality of the included articles, as our primary objective was to consolidate and synthesize studies that specifically focused on the application of ECAs in oncology research.Despite these limitations, our scoping review provides a valuable overview of the current landscape of ECAs in oncology research.

| CONCLUSION
This scoping review examines the current applications of ECAs in oncology research, reflecting on the methodological diversity and the evolving landscape of clinical trials.Our findings reveal a significant geographical spread in ECA usage, predominantly in studies from the United States and Japan.Notably, the primary data sources for ECAs included pooled data from past trials, administrative health databases, and electronic medical records.This diversity in data sourcing underscores the flexibility of ECAs but also introduces potential variabilities that can affect the quality and comparability of the results.While ECAs offer an invaluable resource in situations where traditional RCTs are not feasible, our review also underscores the methodological heterogeneity and the challenges it poses for comparative effectiveness research.
Based on the identified literature base, it is evident that there is a critical need for establishing robust guidelines that address the selection of appropriate data sources, the application of statistical methods for matching, and the overall design and execution of ECAs.Furthermore, guidelines for the consistent reporting of ECAs within the literature are lacking and needed.Such guidelines would not only improve the uptake of ECAs but also enhance their utility in regulatory and clinical decision-making processes.

value
Sun et al., 2020 a Age, BMI, nodal status, prior AI received, prior chemotherapy received Propensity score matching Method 1: 3-year breast cancer free interval The time from enrollment in the study to the first invasive breast cancer event (local, regional, or distant recurrence or a new invasive contralateral breast cancer) from any cause and graft rejection were considered events of interest.Calculated from date of HSCT until either relapse, death, or date of last follow-up alive and in complete remission

F I G U R E 1
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram for the literature search and study selection.

T A B L E 1
Overview of the 23 studies utilizing synesthetic control arms in various cancer types, stages, and data sources.

Cancer site Cancer type Cancer stage(s) Source of real-world data Study design Country
Usmani et al.Weisel et al. a Cox et al.Cupples et al.Sun et al. a Jo et al.Narita et al.Omidvari et al. (Continues) each contributed to 17% (4/23) of the studies.An additional 13% (3/23) of controls were derived Note: Advanced bile duct cancer: Stages III-IV; Advanced multiple cancers: Stage IV; Advanced renal cancer: Stages III-IV; Early-stage GI cancer: Stages I-II; Metastatic colorectal cancer: Stage IV.Abbreviation: NR, Not reported.a Article manually added.
Summary of design and statistical parameters including covariate adjustments, methods, and outcomes for the 23 included synesthetic control arm studies.
283.8 | Line of therapyWith respect to the line of therapy used in the trial intervention, 50% (9/18) evaluated first-line(1 L)
a Article manually added.